code: def test_X_PCA_but_no_explained_variance():
      with pytest.raises(
          ValueError,
          match="If `X_pca` is not None, the `explained variance` "
          "values should not be `None`.",
      ):
          X, y = iris_data()
          pca = PCA(n_components=2)
          X_pca = pca.fit_transform(X)
  
          plot_pca_correlation_graph(
              X,
              variables_names=["1", "2", "3", "4"],
              X_pca=X_pca,
              explained_variance=None,
          )
  
file_path: mlxtend/mlxtend/plotting/tests/test_pca_corr_graph.py
imports: import pytest
  from packaging.version import Version
  from sklearn import __version__
  from mlxtend.data import iris_data
  from mlxtend.plotting import plot_pca_correlation_graph
label_desc: Use image node_id 3 for calling a global function with example usage: test_X_PCA_but_no_explained_variance() without return types